In the construction industry, cost estimates are fundamental to the success of a construction project. Location factors are commonly used to adjust cost estimates by project location. However, not all locations have corresponding factors. Nowadays, the construction industry has employed a simple, proximity-based location factor interpolation method which is widely accepted and used. Under this method, for a location without adjustment factor, the factor of the geographically “nearest neighbor” will be selected. Although this approach was statistically substantiated by former research, it was still not sufficiently supported, considering that only one year’s RSMeans City Cost Index (CCI) dataset was tested. With the help of the Global Moran’s I Test in ArcGIS software, this study evaluated the spatial autocorrelation of the changes in RSMeans CCI value from year 2005 to 2009. The evaluation results substantially supported the validity of the proximity-based location factor interpolation method. In addition, evaluation of current and alternative surface interpolation methods reveals that condition nearest neighbor (CNN) method is the best rough surface interpolation method while inverse distance weighted (IDW) method is the best smooth surface interpolation method. Moreover, the Area Cost Factor (ACF) of the Department of Defense (DoD) was incorporated in this research to cross-validate all evaluations. This research is an initial step for identifying surface interpolation methods to develop spatial prediction models for location adjustment based upon several datasets, including construction cost data and socio-economical data.